Data Analytics and Recruitment Automation

Data-driven recruitment is when recruiters optimize & streamline the entire recruitment journey leveraging available data instead of human intuition or assumptions. Data analytics is implemented across the recruitment lifecycle to source, screen, evaluate & onboard the best quality candidates without any bias or possible human errors.  Data analytics will help recruiters to analyze a large talent pool to identify and onboard the best quality candidates with the right skills and experiences to help the organization achieve its goals. 

Predictive analytics in recruitment is the process of using historical data to make predictions about future hiring activities and candidates. It includes collecting and analyzing data using statistics, machine learning, and modeling techniques to best predict what could happen under specific situations.  For instance, the recruiter can use predictive analytics to determine if the candidate will be a good or bad hire. It does so by analyzing the candidate’s external inputs gathered by the ATS, from sources like their CV, cover letter, assessments and pre-screens, social media, etc.

To better understand the significance of predictive analytics, let’s take a look at some of the areas that it provides insight into:

• Candidate sourcing: which are your most effective sourcing platforms (job boards, social media, referrals, and so on).
• Candidate screening: the duration of the process, which candidate screening techniques are effective, and which aren’t.
•  Candidate drop-off: how long it takes to go from the application stage to the onboarding stage, and what effect that has on drop-off rates.
• Future employment needs: what positions will be in demand or become vacant in the future.
• Future employee performance: how likely a candidate or new hire will perform well on the job.
• The urgency of hiring: which roles and skills are needed most urgently to meet company needs.
• Retention rates: how long a new hire will stay with or leave the company.
• Hiring bottlenecks: investigate where roadblocks in the hiring process regularly occur, their impact, and how to fix them.

1. Improves quality of hire: Depending on the data analysis to onboard a candidate rather than human intuitions and assumptions will acquire quality candidates. Data analysis provides meaningful insights that help recruiters make informed decisions. 
2. Reduces the cost of hiring: Data analysis will help recruiters & business leaders gain a comprehensive view of the entire recruitment activities & let them understand the process where there is an inefficiency & unwanted cost incurred, enabling them to rectify this on the go & become more cost-effective. 
3. Improves candidate experience: Candidate experience determines the success rate of any recruitment efforts. When the process is broken, complex, and not optimized for each candidate, they will drop off or the competitors will acquire them at ease. It’s all about how recruiters or systems craft an impeccable experience for candidates to emerge as winners. 
4.  Proactive Hiring: A recruiter can identify inefficiencies in the hiring stages and predict the time of hire for a particular skill by measuring the average time a candidate spends on each phase of the hiring cycle. Recruiters can also determine which employees may leave shortly, which can help them in making predictions about the organizational departments that will soon require resources. 

The objective of data-driven recruitment is to leverage a large amount of data available with the recruiters to optimize and streamline the entire recruitment process and hire the best quality candidates. In addition to helping recruiters with accurate hiring, data analytics also make sure the recruitment process is more efficient and cost-effective.